Instrumental and socioemotional communications in doctor-patient interactions in urban and rural clinics
Bibliographic record
Abstract
BACKGROUND: Location of practice, such as working in a rural or urban clinic, may influence how physicians communicate with their patients. This exploratory pilot study examines the communication styles used during doctor-patient interactions in urban and rural family practice settings in Western Canada. METHODS: We analyzed observation and interview data from four physicians practicing in these different locations. Using a grounded theory approach, communications were categorized as either instrumental or socioemotional. Instrumental communication refers to "cure-oriented interactions" and tends to be more task-oriented focusing on the patient's health concerns and reason for the appointment. In contrast, socioemotional communication refers to more "care-oriented interactions" that may make the patient feel comfortable, relieve patient anxiety and build a trusting relationship. RESULTS: The physicians in small, rural towns appear to know their patients and their families on a more personal level and outside of their office, and engage in more socioemotional communications compared to those practicing in suburban clinics in a large urban centre. Knowing patients outside the clinic seems to change the nature of the doctor-patient interaction, and, in turn, the doctor-patient relationship itself. Interactions between urban doctors and their patients had a mixture of instrumental and socioemotional communications, while interactions between rural doctors and their patients tended to be highly interpersonal, often involving considerable socioemotional communication and relationship-building. CONCLUSIONS: Despite the different ways that doctors and patients communicate with each other in the two settings, rural and urban doctors spend approximately the same amount of time with their patients. Thus, greater use of socioemotional communication by rural doctors, which may ease patient anxiety and increase patient trust, did not appear to add extra time to the patient visit. Research suggests that socioemotional communication may ultimately lead to better patient outcomes, which implies that health differences between rural and urban settings could be linked to differences in doctor-patient communication styles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".